Application of a Bayesian strategy to ABCD: Identification of substance-use risk and COVID-19 effects on neurodevelopment

Since use during adolescence is common, however earlier ages of initiation are associated with numerous negative consequences, including increased likelihood of a substance use disorder in later life. Multiple lines of evidence indicate that individual differences in neural development contribute to vulnerabilities for earlier ages of substance use initiation. However, the neural basis of this at the network level is not well understood. The Adolescent Brain and Cognitive Development (ABCD) study provides an unprecedented opportunity for assessment of neural networks of substance-use risk. However, children in this cohort now face a unique developmental challenge: entering adolescence during the COVID-19 pandemic and associated response measures. Here, we propose a novel Bayesian framework to accelerate, enrich and individualize this assessment. In direct response to PAR-19-162 ('Accelerating the Pace of Child Health Research Using Existing Data from the ABCD Study'), our proposal will establish, refine, and deploy Bayesian hierarchical longitudinal modeling tools to carefully quantify deviation from normative development in brain trajectories relevant to substance-use risk in the ABCD dataset with careful consideration of COVID-19 effects.